Although different network architectures are explored, these deep learning based methods share two common issues, i.e., limited scene prior and contextual information utilization. Firstly, only the original version of a haze image is fed to the dehazing network. This ignores the underlying scene prior of the input image, which could be captured from its multiple versions under different exposure conditions. Secondly, existing methods mainly cope with non-homogeneous haze by selectively preserving the features of each local region with the guidance of transmission or attention maps, which fail to explicitly utilize the information from the other distant regions, even they contain important scene prior. As shown in Fig. 1, the scattering model based deep network DCPDN [1]} is unable to remove the haze, while fully end-to-end deep network GridNet [2]} has limited ability to restore details and colors for non-homogeneous haze.
<FIGURE>